Dataset Information

MolabIS--an integrated information system for storing and managing molecular genetics data.

ABSTRACT:

Background

Long-term sample storage, tracing of data flow and data export for subsequent analyses are of great importance in genetics studies. Therefore, molecular labs do need a proper information system to handle an increasing amount of data from different projects.

Results

We have developed a molecular labs information management system (MolabIS). It was implemented as a web-based system allowing the users to capture original data at each step of their workflow. MolabIS provides essential functionality for managing information on individuals, tracking samples and storage locations, capturing raw files, importing final data from external files, searching results, accessing and modifying data. Further important features are options to generate ready-to-print reports and convert sequence and microsatellite data into various data formats, which can be used as input files in subsequent analyses. Moreover, MolabIS also provides a tool for data migration.

Conclusions

MolabIS is designed for small-to-medium sized labs conducting Sanger sequencing and microsatellite genotyping to store and efficiently handle a relative large amount of data. MolabIS not only helps to avoid time consuming tasks but also ensures the availability of data for further analyses. The software is packaged as a virtual appliance which can run on different platforms (e.g. Linux, Windows). MolabIS can be distributed to a wide range of molecular genetics labs since it was developed according to a general data model. Released under GPL, MolabIS is freely available at http://www.molabis.org.

SUBMITTER: Truong CV

PROVIDER: S-EPMC3268772 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MolabIS--an integrated information system for storing and managing molecular genetics data.

Truong Cong V C CV Groeneveld Linn F LF Morgenstern Burkhard B Groeneveld Eildert E

BMC bioinformatics 20111031

<h4>Background</h4>Long-term sample storage, tracing of data flow and data export for subsequent analyses are of great importance in genetics studies. Therefore, molecular labs do need a proper information system to handle an increasing amount of data from different projects.<h4>Results</h4>We have developed a molecular labs information management system (MolabIS). It was implemented as a web-based system allowing the users to capture original data at each step of their workflow. MolabIS provide ...[more]

PMID: 22040322

Similar Datasets

Project description:BackgroundNew "next generation" DNA sequencing technologies offer individual researchers the ability to rapidly generate large amounts of genome sequence data at dramatically reduced costs. As a result, a need has arisen for new software tools for storage, management and analysis of genome sequence data. Although bioinformatic tools are available for the analysis and management of genome sequences, limitations still remain. For example, restrictions on the submission of data and use of these tools may be imposed, thereby making them unsuitable for sequencing projects that need to remain in-house or proprietary during their initial stages. Furthermore, the availability and use of next generation sequencing in industrial, governmental and academic environments requires biologist to have access to computational support for the curation and analysis of the data generated; however, this type of support is not always immediately available.ResultsTo address these limitations, we have developed DraGnET (Draft Genome Evaluation Tool). DraGnET is an open source web application which allows researchers, with no experience in programming and database management, to setup their own in-house projects for storing, retrieving, organizing and managing annotated draft and complete genome sequence data. The software provides a web interface for the use of BLAST, allowing users to perform preliminary comparative analysis among multiple genomes. We demonstrate the utility of DraGnET for performing comparative genomics on closely related bacterial strains. Furthermore, DraGnET can be further developed to incorporate additional tools for more sophisticated analyses.ConclusionsDraGnET is designed for use either by individual researchers or as a collaborative tool available through Internet (or Intranet) deployment. For genome projects that require genome sequencing data to initially remain proprietary, DraGnET provides the means for researchers to keep their data in-house for analysis using local programs or until it is made publicly available, at which point it may be uploaded to additional analysis software applications. The DraGnET home page is available at http://www.dragnet.cvm.iastate.edu and includes example files for examining the functionalities, a link for downloading the DraGnET setup package and a link to the DraGnET source code hosted with full documentation on SourceForge.

Project description:The datasets and tool presented in this article are related to the research article entitled "Improving crash predictability of the Highway Safety Manual through optimizing local calibration process" (Dadvar et al., 2020) [1], in which these datasets were used to investigate alternative local calibration methods for the Highway Safety Manual (HSM) [2] local application. The datasets are integrated Highway Safety Information System (HSIS) [3] state-wide rural two-lane, two-way roads (R2U) data from states of Illinois and Washington. The HSIS is a database that maintains motor vehicle crash data, roadway inventory, and traffic volume data for several US states. It is an excellent source of data to highway safety research and can be used to investigate many research questions. However, to prepare an analysis-ready roadway safety dataset based on the HSIS or any databases that store the relevant data in multiple different datasets, the researchers should integrate multiple datasets, merge or unmerge and remove certain inconsistent records, and finally clean the integrated dataset. The HSIS staff is usually accommodating and eager to help, but sometimes the nature of data needs is complicated and laborious. A tool named Roadway Safety Data Integrator (RSDI) was developed for combining, segmenting, and selecting homogeneous (unchanged during the study period for certain variables of interest) HSIS roadway segments and also crash assignment by desired crash attributes (e.g., crash severity or type). The RSDI tool can be helpful for integrating different safety-related datasets such as roadway inventory (including grade, curve, and other subsets), traffic volume, and motor vehicle crash data; also, it can do required segmentation and identify the homogeneous roadway segments over the desired years of study that are the basis for development and calibration of the HSM predictive models. The shared datasets contain homogeneous roadway segments, geometric details, and crash data for six years from Illinois (2005-10) and Washington (2010-15). The datasets and RSDI tool would be important sources generally for investigating highway safety research questions and in particular, for HSM-related analyses. The RSDI tool can be used for similar purposes and it is not limited to the HSIS data. It can be used for segmentation and finding homogeneous segments of any datasets that follow linear referencing. The datasets and RSDI tool are hosted in the Mendeley Data repository [4].

Dataset Information

MolabIS--an integrated information system for storing and managing molecular genetics data.

Background

Results

Conclusions

Publications

MolabIS--an integrated information system for storing and managing molecular genetics data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets